Tool for Predicting Glycosaminoglycan Recognition of Proteins

预测蛋白质糖胺聚糖识别的工具

基本信息

批准号：
9813586
负责人：
Umesh Ramanlal Desai
金额：
$ 28.19万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-03 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9813586
关键词：
Address Affinity Algorithms Amino Acid Sequence Antithrombins Binding Binding Proteins Biochemical Biological CXCL13 gene Chondroitin Sulfates Coagulation Process Communities Computer Analysis Computer Simulation Dermatan Sulfate Development EP300 gene FGF1 gene FGFR1 gene Focus Groups Generations Glycobiology Glycosaminoglycans Growth Heparin Heparin Cofactor II Heparitin Sulfate Human Individual Inflammation Ireland Knowledge Lead Leukocyte Elastase Libraries Morphogenesis Nature Oligonucleotides Oligosaccharides Outcome Output Polysaccharides Proteins Publications Research Research Personnel Role Site Specialist Specificity Structure System Technology Thrombin Time Transforming Growth Factors Work biophysical techniques cationic antimicrobial protein CAP 37 chemokine computer code computerized tools dermatan sulfate chondroitin sulfate design experience graphical user interface innovation interest milligram multiple myeloma M Protein operation predictive tools receptor software development tool user-friendly web server web-enabled

项目摘要

PROJECT SUMMARY GAGs present considerable structural diversity, which has made the study of individual GAG sequences humanly impossible. Most studies performed to date rely on heterogeneous GAG compositions, such as heparin and chondroitin sulfate. Few dozen GAG oligosaccharides have become commercially available in recent times (Sigma (US), Dextra (UK), and Iduron (UK)). Yet, purchasing even a small, reasonably diverse library of these oligosaccharides is very expensive (~$200–300 for few µg to mg each). More importantly, the oligosaccharides available from these companies are generally the common sequences and do not represent the diversity present in nature. Synthesis of GAG oligosaccharides is challenging and only a handful of groups have experience with synthesis technology. We have developed a computational tool that helps predict key GAG sequence that recognize protein with high affinity. Our tool has been validated for proteins including antithrombin, fibroblast growth factor-1 & its receptor (FGF-1/FGFR1), transforming growth factor 2 (TGF2), thrombin, histone acetyltransferase p300, human neutrophil elastase and chemokine CXCL13. We propose to make this tool freely available to the research community so that many groups can computationally assess whether their protein of interest binds GAGs. Our two aims include 1) develop a graphical user interface (GUI) on a web-server to enable researchers utilize our computational tool for studying GAG–protein interactions; and 2) advance the computational tool for predicting the interaction of commercially available GAG sequences (HP/HS and CS/DS) with proteins. These two aims directly address the RFA by making our in-house tool “significantly more straightforward and accessible for non-specialists”. In terms of output, this work will put forward a web- enabled tool carrying libraries of GAG sequences and appropriate algorithms for use by researchers from remote sites. It will add to the continuing democratization of glycan tools to enable more effective glycan research. In terms of knowledge contribution, our computational tool would help enhance understanding on how GAGs are recognized by proteins, especially those belonging to coagulation, inflammation and growth/morphogenesis systems.

项目概要 GAG 呈现出相当大的结构多样性，这使得对单个 GAG 序列的研究迄今为止进行的大多数研究都依赖于异质 GAG 成分，例如肝素。近年来，已有几十种 GAG 寡糖上市销售。（Sigma（美国）、Dextra（英国）和 Iduron（英国）），甚至购买一个小型的、相当多样化的库）。低聚糖非常昂贵（每个几微克到毫克约 200-300 美元）。这些公司提供的产品通常是通用序列，并不代表存在的多样性在自然界中，GAG 寡糖的合成具有挑战性，只有少数团队拥有这方面的经验。我们开发了一种计算工具，可以帮助预测关键的 GAG 序列。我们的工具已针对包括抗凝血酶、成纤维细胞在内的蛋白质进行了验证。生长因子-1 及其受体 (FGF-1/FGFR1)、转化生长因子 2 (TGF2)、凝血酶、组蛋白乙酰转移酶 p300、人中性粒细胞弹性蛋白酶和趋化因子 CXCL13 我们建议免费制作此工具。可供研究界使用，以便许多团体可以通过计算评估他们的蛋白质是否我们的两个目标包括 1) 在 Web 服务器上开发图形用户界面 (GUI) 使研究人员能够利用我们的计算工具来研究 GAG-蛋白质相互作用；2) 推进用于预测市售GAG序列相互作用的计算工具（HP/HS 和 CS/DS）与蛋白质，这两个目标通过使我们的内部工具“显着”来直接解决 RFA 问题。就输出而言，这项工作将提出一个网络- 启用工具，携带 GAG 序列库和适当的算法，供远程研究人员使用它将促进聚糖工具的持续民主化，以实现更有效的聚糖研究。就知识贡献而言，我们的计算工具将有助于增强对 GAG 的理解被蛋白质识别，特别是那些属于凝血、炎症和生长/形态发生的蛋白质系统。